-
CiteScore
-
Impact Factor
Volume 1, Issue 1, ICCK Transactions on Systems Safety and Reliability
Volume 1, Issue 1, 2025
Submit Manuscript Edit a Special Issue
Article QR Code
Article QR Code
Scan the QR code for reading
Popular articles
ICCK Transactions on Systems Safety and Reliability, Volume 1, Issue 1, 2025: 4-20

Free to Read | Review Article | 30 July 2025
The State-of-the-Art Development and New Challenges: Operations Management of Metro Systems
1 School of Economics & Management, Beijing Forestry University, Beijing 100083, China
2 Academy of Mathematics & Systems Science, Chinese Academy of Sciences, Beijing 100190, China
3 Department of Logistics and Maritime Studies, The Hong Kong Polytechnic University, Hong Kong, China
4 School of Management Science and Engineering, Central University of Finance and Economics, Beijing 100081, China
5 School of Economics & Management, Beijing University of Technology, Beijing 100081, China
6 Kent Business School, University of Kent, Canterbury, Kent CT2 7FS, United Kingdom
* Corresponding Author: Rui Peng, [email protected]
Received: 15 May 2025, Accepted: 06 July 2025, Published: 30 July 2025  
Abstract
This paper comprehensively reviews literature on the operations management of metro systems, which are crucial for urban mass transit. It classifies the existing research into five categories: 1) passenger demand prediction; 2) timetabling and scheduling; 3) system vulnerability, resilience and performance; 4) resource planning; and 5) evacuation optimization. The paper focuses on publications in the last decade in order to reflect the latest research and industrial trends. In addition, some limitations of the existing literature are located and the potential knowledge gaps are identified. The paper provides a useful reference for developing sustainable and resilient metro systems to meet the needs of expanding cities while maintaining high standards of safety, reliability, and efficiency.

Graphical Abstract
The State-of-the-Art Development and New Challenges: Operations Management of Metro Systems

Keywords
metro systems
operations management
resource optimization
sustainable development

Data Availability Statement
Data will be made available on request.

Funding
The work was supported by the National Natural Science Foundation of China under Grant 72001027, the Postdoctoral Foundation of China under Grant 2021M693331, and the Fundamental Research Funds for the Central Universities.

Conflicts of Interest
The authors declare no conflicts of interest.

Ethical Approval and Consent to Participate
Not applicable.

References
  1. Yu, C., Li, H., Xu, X., & Liu, J. (2020). Data-driven approach for solving the route choice problem with traveling backward behavior in congested metro systems. Transportation Research Part E: Logistics and Transportation Review, 142, 102037.
    [CrossRef]   [Google Scholar]
  2. Wang, L., Jin, J. G., Sun, L., & Lee, D. H. (2024). Urban rail transit disruption management: Research progress and future directions. Frontiers of Engineering Management, 11(1), 79-91.
    [CrossRef]   [Google Scholar]
  3. Yang, X., Li, X., Ning, B., & Tang, T. (2015). A survey on energy-efficient train operation for urban rail transit. IEEE Transactions on Intelligent Transportation Systems, 17(1), 2-13.
    [CrossRef]   [Google Scholar]
  4. Xu, B., & Hao, J. (2017). Air quality inside subway metro indoor environment worldwide: A review. Environment international, 107, 33-46.
    [CrossRef]   [Google Scholar]
  5. Chen, H., Chen, B., Zhang, L., & Li, H. X. (2021). Vulnerability modeling, assessment, and improvement in urban metro systems: A probabilistic system dynamics approach. Sustainable Cities and Society, 75, 103329.
    [CrossRef]   [Google Scholar]
  6. Chen, J., Pu, Z., Guo, X., Cao, J., & Zhang, F. (2023). Multiperiod metro timetable optimization based on the complex network and dynamic travel demand. Physica A: Statistical Mechanics and its Applications, 611, 128419.
    [CrossRef]   [Google Scholar]
  7. Diab, E., & Shalaby, A. (2020). Metro transit system resilience: Understanding the impacts of outdoor tracks and weather conditions on metro system interruptions. International Journal of Sustainable Transportation, 14(9), 657-670.
    [CrossRef]   [Google Scholar]
  8. Du, Z., Tang, J., Qi, Y., Wang, Y., Han, C., & Yang, Y. (2020). Identifying critical nodes in metro network considering topological potential: A case study in Shenzhen city—China. Physica A: Statistical Mechanics and its Applications, 539, 122926.
    [CrossRef]   [Google Scholar]
  9. Fernandes, T., Magalhães, J. P., & Alves, W. (2025). Cybersecurity in Smart Railways: exploring risks, vulnerabilities and mitigation in the data communication services. Green Energy and Intelligent Transportation, 100305.
    [CrossRef]   [Google Scholar]
  10. Fu, X., Zuo, Y., Wu, J., Yuan, Y., & Wang, S. (2022). Short-term prediction of metro passenger flow with multi-source data: A neural network model fusing spatial and temporal features. Tunnelling and Underground Space Technology, 124, 104486.
    [CrossRef]   [Google Scholar]
  11. Geng, J., Zhang, C., Yang, L., Meng, F., & Qi, J. (2024). Integrated scheduling of metro trains and shuttle buses with passenger flow control strategy on an oversaturated metro line. Computers & Industrial Engineering, 189, 109980.
    [CrossRef]   [Google Scholar]
  12. Gohar, A., & Nencioni, G. (2021). The role of 5G technologies in a smart city: The case for intelligent transportation system. Sustainability, 13(9), 5188.
    [CrossRef]   [Google Scholar]
  13. Guo, K., & Zhang, L. (2022). Adaptive multi-objective optimization for emergency evacuation at metro stations. Reliability Engineering & System Safety, 219, 108210.
    [CrossRef]   [Google Scholar]
  14. Hamid, B., Jhanjhi, N. Z., Humayun, M., Khan, A., & Alsayat, A. (2019, December). Cyber security issues and challenges for smart cities: A survey. In 2019 13th International Conference on Mathematics, Actuarial Science, Computer Science and Statistics (MACS) (pp. 1-7). IEEE.
    [CrossRef]   [Google Scholar]
  15. He, R., Zhang, L., & Tiong, R. L. (2023). Flood risk assessment and mitigation for metro stations: An evidential-reasoning-based optimality approach considering uncertainty of subjective parameters. Reliability Engineering & System Safety, 238, 109453.
    [CrossRef]   [Google Scholar]
  16. Hu, Y., Li, S., Dessouky, M. M., Yang, L., & Gao, Z. (2022). Computationally efficient train timetable generation of metro networks with uncertain transfer walking time to reduce passenger waiting time: A generalized Benders decomposition-based method. Transportation Research Part B: Methodological, 163, 210-231.
    [CrossRef]   [Google Scholar]
  17. Iseki, H., Liu, C., & Knaap, G. (2018). The determinants of travel demand between rail stations: A direct transit demand model using multilevel analysis for the Washington DC Metrorail system. Transportation Research Part A: Policy and Practice, 116, 635-649.
    [CrossRef]   [Google Scholar]
  18. Jing, W., Xu, X., & Pu, Y. (2020). Route redundancy-based approach to identify the critical stations in metro networks: A mean-excess probability measure. Reliability Engineering & System Safety, 204, 107204.
    [CrossRef]   [Google Scholar]
  19. Kong, G., Hu, S., & Yang, Q. (2023). Uncertainty method and sensitivity analysis for assessment of energy consumption of underground metro station. Sustainable Cities and Society, 92, 104504.
    [CrossRef]   [Google Scholar]
  20. Kopsidas, A., & Kepaptsoglou, K. (2022). Identification of critical stations in a Metro System: A substitute complex network analysis. Physica A: Statistical Mechanics and its Applications, 596, 127123.
    [CrossRef]   [Google Scholar]
  21. Kuppusamy, P., Venkatraman, S., Rishikeshan, C. A., & Reddy, Y. P. (2020). Deep learning based energy efficient optimal timetable rescheduling model for intelligent metro transportation systems. Physical Communication, 42, 101131.
    [CrossRef]   [Google Scholar]
  22. Li, H., Jin, K., Sun, S., Jia, X., & Li, Y. (2022). Metro passenger flow forecasting though multi-source time-series fusion: An ensemble deep learning approach. Applied Soft Computing, 120, 108644.
    [CrossRef]   [Google Scholar]
  23. Li, J., Li, X., Chen, D., & Godding, L. (2018). Assessment of metro ridership fluctuation caused by weather conditions in Asian context: Using archived weather and ridership data in Nanjing. Journal of transport geography, 66, 356-368.
    [CrossRef]   [Google Scholar]
  24. Li, M., Kwan, M. P., Yin, J., Yu, D., & Wang, J. (2018). The potential effect of a 100-year pluvial flood event on metro accessibility and ridership: A case study of central Shanghai, China. Applied geography, 100, 21-29.
    [CrossRef]   [Google Scholar]
  25. Li, X., Liu, Y., Gao, Z., & Liu, D. (2016). Linkage between passenger demand and surrounding land-use patterns at urban rail transit stations: A canonical correlation analysis method and case study in Chongqing. International Journal of Transportation Science and Technology, 5(1), 10-16.
    [CrossRef]   [Google Scholar]
  26. Li, X., Liu, Y., & Zhang, Q. (2024). Festival timetable synchronization of metro trains and high-speed railway trains for late-night operations: an integrated bi-directional transfer optimization model. Transportation Letters, 16(10), 1252-1267.
    [CrossRef]   [Google Scholar]
  27. Liang, J., Zang, G., Liu, H., Zheng, J., & Gao, Z. (2023). Reducing passenger waiting time in oversaturated metro lines with passenger flow control policy. Omega, 117, 102845.
    [CrossRef]   [Google Scholar]
  28. Lin, S., Fang, X., Lin, F., Yang, Z., & Wang, X. (2018). Reliability of rail transit traction drive system-A review. Microelectronics Reliability, 88, 1281-1285.
    [CrossRef]   [Google Scholar]
  29. Liu, Q., He, R., & Zhang, L. (2022). Simulation-based multi-objective optimization for enhanced safety of fire emergency response in metro stations. Reliability Engineering & System Safety, 228, 108820.
    [CrossRef]   [Google Scholar]
  30. Liu, W., Shao, Y., Li, C., Li, C., & Jiang, Z. (2023). Development of a non-Gaussian copula Bayesian network for safety assessment of metro tunnel maintenance. Reliability Engineering & System Safety, 238, 109423.
    [CrossRef]   [Google Scholar]
  31. Lu, K., Han, B., & Zhou, X. (2018). Smart urban transit systems: from integrated framework to interdisciplinary perspective. Urban Rail Transit, 4(2), 49-67.
    [CrossRef]   [Google Scholar]
  32. Lu, Y., Yang, L., Yang, H., Zhou, H., & Gao, Z. (2023). Robust collaborative passenger flow control on a congested metro line: A joint optimization with train timetabling. Transportation Research Part B: Methodological, 168, 27-55.
    [CrossRef]   [Google Scholar]
  33. Lyu, H. M., Shen, S. L., Zhou, A., & Yang, J. (2019). Perspectives for flood risk assessment and management for mega-city metro system. Tunnelling and Underground Space Technology, 84, 31-44.
    [CrossRef]   [Google Scholar]
  34. Ma, X., Zhang, J., Ding, C., & Wang, Y. (2018). A geographically and temporally weighted regression model to explore the spatiotemporal influence of built environment on transit ridership. Computers, Environment and Urban Systems, 70, 113-124.
    [CrossRef]   [Google Scholar]
  35. Mahomed, A. S., & Saha, A. K. (2025). Unleashing the Potential of 5G for Smart Cities: A Focus on Real-Time Digital Twin Integration. Smart Cities, 8(2), 70.
    [CrossRef]   [Google Scholar]
  36. Mirbod, M., & Dehghani, H. (2023). Smart trip prediction model for metro traffic control using data mining techniques. Procedia Computer Science, 217, 72-81.
    [CrossRef]   [Google Scholar]
  37. Nian, G., Chen, F., Li, Z., Zhu, Y., & Sun, D. (2019). Evaluating the alignment of new metro line considering network vulnerability with passenger ridership. Transportmetrica A: Transport Science, 15(2), 1402-1418.
    [CrossRef]   [Google Scholar]
  38. Nwamekwe, C. O., & Chikwendu, O. C. (2025). Machine learning-augmented digital twin systems for predictive maintenance in highspeed rail networks. International Journal of Multidisciplinary Research and Growth Evaluation, 6(01), 1783-1795.
    [Google Scholar]
  39. Pan, H., Li, J., Shen, Q., & Shi, C. (2017). What determines rail transit passenger volume? Implications for transit oriented development planning. Transportation Research Part D: Transport and Environment, 57, 52-63.
    [CrossRef]   [Google Scholar]
  40. Pei, M., Xu, M., Zhong, L., & Qu, X. (2023). Robust design for underground metro systems with modular vehicles. Tunnelling and Underground Space Technology, 132, 104865.
    [CrossRef]   [Google Scholar]
  41. Prabhakaran, P., Anandakumar, S., Priyanka, E. B., & Thangavel, S. (2023). Development of service quality model computing ridership of metro rail system using fuzzy system. Results in Engineering, 17, 100946.
    [CrossRef]   [Google Scholar]
  42. Raveau, S., Guo, Z., Muñoz, J. C., & Wilson, N. H. (2014). A behavioural comparison of route choice on metro networks: Time, transfers, crowding, topology and socio-demographics. Transportation Research Part A: Policy and Practice, 66, 185-195.
    [CrossRef]   [Google Scholar]
  43. Sengan, S., Subramaniyaswamy, V., Nair, S. K., Indragandhi, V., Manikandan, J., & Ravi, L. (2020). Enhancing cyber–physical systems with hybrid smart city cyber security architecture for secure public data-smart network. Future generation computer systems, 112, 724-737.
    [CrossRef]   [Google Scholar]
  44. Sharma, M. K., & Chauhan, B. K. (2022). Timetable rationalization & Operational improvements by human intervention in an urban rail transit system: An exploratory study. Transportation Research Interdisciplinary Perspectives, 13, 100526.
    [CrossRef]   [Google Scholar]
  45. Shi, J., Yang, J., Yang, L., Tao, L., Qiang, S., Di, Z., & Guo, J. (2023). Safety-oriented train timetabling and stop planning with time-varying and elastic demand on overcrowded commuter metro lines. Transportation research part E: logistics and transportation review, 175, 103136.
    [CrossRef]   [Google Scholar]
  46. Su, B., D’Ariano, A., Su, S., Wang, X., & Tang, T. (2023). Integrated train timetabling and rolling stock rescheduling for a disturbed metro system: A hybrid deep reinforcement learning and adaptive large neighborhood search approach. Computers & industrial engineering, 186, 109742.
    [CrossRef]   [Google Scholar]
  47. Tang, Y., Bi, W., Varga, L., Dolan, T., & Li, Q. (2022). An integrated framework for managing fire resilience of metro station system: Identification, assessment, and optimization. International Journal of Disaster Risk Reduction, 77, 103037.
    [CrossRef]   [Google Scholar]
  48. Tessitore, M. L., Sama, M., D’Ariano, A., Hélouët, L., & Pacciarelli, D. (2022). A simulation-optimization framework for traffic disturbance recovery in metro systems. Transportation research part C: emerging technologies, 136, 103525.
    [CrossRef]   [Google Scholar]
  49. Wan, X., Li, Q., Yuan, J., & Schonfeld, P. M. (2015). Metro passenger behaviors and their relations to metro incident involvement. Accident Analysis & Prevention, 82, 90-100.
    [CrossRef]   [Google Scholar]
  50. Wang, B., Xie, S., Jiang, C., Song, Q., Sun, S., & Wang, X. (2020). An investigation into the fatigue failure of metro vehicle bogie frame. Engineering Failure Analysis, 118, 104922.
    [CrossRef]   [Google Scholar]
  51. Banerjee, A., Costa, B., Forkan, A. R. M., Kang, Y. B., Marti, F., McCarthy, C., ... & Jayaraman, P. P. (2024). 5G enabled smart cities: A real-world evaluation and analysis of 5G using a pilot smart city application. Internet of Things, 28, 101326.
    [CrossRef]   [Google Scholar]
  52. Wang, Y., Chen, J., Qin, Y., & Yang, X. (2023). Timetable rescheduling of metro network during the last train period. Tunnelling and Underground Space Technology, 139, 105226.
    [CrossRef]   [Google Scholar]
  53. Wu, X., Dong, H., Tse, C. K., Ho, I. W., & Lau, F. C. (2018). Analysis of metro network performance from a complex network perspective. Physica A: Statistical Mechanics and its Applications, 492, 553-563.
    [CrossRef]   [Google Scholar]
  54. Wu, Z., Sun, J., & Xu, R. (2019). Calculating vulnerability index of urban metro systems based on satisfied route. Physica A: Statistical Mechanics and Its Applications, 531, 121722.
    [CrossRef]   [Google Scholar]
  55. Xiu, C., Sun, Y., & Peng, Q. (2022). Modelling traffic as multi-graph signals: Using domain knowledge to enhance the network-level passenger flow prediction in metro systems. Journal of Rail Transport Planning & Management, 24, 100342.
    [CrossRef]   [Google Scholar]
  56. Chakrabarti, S. (2015). The demand for reliable transit service: New evidence using stop level data from the Los Angeles Metro bus system. Journal of Transport Geography, 48, 154-164.
    [CrossRef]   [Google Scholar]
  57. Xu, X. Y., Liu, J., Li, H. Y., & Jiang, M. (2016). Capacity-oriented passenger flow control under uncertain demand: Algorithm development and real-world case study. Transportation Research Part E: Logistics and Transportation Review, 87, 130-148.
    [CrossRef]   [Google Scholar]
  58. Xu, Y., & Chen, X. (2023). Uncovering the relationship among spatial vitality, perception, and environment of urban underground space in the metro zone. Underground Space, 12, 167-182.
    [CrossRef]   [Google Scholar]
  59. Yang, C., Yan, F., & Ukkusuri, S. V. (2018). Unraveling traveler mobility patterns and predicting user behavior in the Shenzhen metro system. Transportmetrica A: Transport Science, 14(7), 576-597.
    [CrossRef]   [Google Scholar]
  60. Chai, S., Yin, J., D’Ariano, A., Samà, M., & Tang, T. (2023). Train schedule optimization for commuter-metro networks. Transportation research part C: emerging technologies, 155, 104278.
    [CrossRef]   [Google Scholar]
  61. Yang, X., Wu, J., Sun, H., Gao, Z., Yin, H., & Qu, Y. (2019). Performance improvement of energy consumption, passenger time and robustness in metro systems: A multi-objective timetable optimization approach. Computers & Industrial Engineering, 137, 106076.
    [CrossRef]   [Google Scholar]
  62. Yanık, S., Aktas, E., & Topcu, Y. I. (2017). Traveler satisfaction in rapid rail systems: The case of Istanbul metro. International Journal of Sustainable Transportation, 11(9), 642-658.
    [CrossRef]   [Google Scholar]
  63. Abad, R. P. B., & Fillone, A. M. (2019). Perceived risk of public transport travel during flooding events in Metro Manila, Philippines. Transportation research interdisciplinary perspectives, 2, 100051.
    [CrossRef]   [Google Scholar]
  64. Yuan, J., Gao, Y., Li, S., Liu, P., & Yang, L. (2022). Integrated optimization of train timetable, rolling stock assignment and short-turning strategy for a metro line. European Journal of Operational Research, 301(3), 855-874.
    [CrossRef]   [Google Scholar]
  65. Yuan, Y., Li, S., Liu, R., Yang, L., & Gao, Z. (2023). Decomposition and approximate dynamic programming approach to optimization of train timetable and skip-stop plan for metro networks. Transportation Research Part C: Emerging Technologies, 157, 104393.
    [CrossRef]   [Google Scholar]
  66. Zhang, D., & Wang, X. C. (2014). Transit ridership estimation with network Kriging: A case study of Second Avenue Subway, NYC. Journal of Transport Geography, 41, 107-115.
    [CrossRef]   [Google Scholar]
  67. Zhang, J., Wang, S., & Wang, X. (2018). Comparison analysis on vulnerability of metro networks based on complex network. Physica A: Statistical Mechanics and its Applications, 496, 72-78.
    [CrossRef]   [Google Scholar]
  68. Zhang, J., & Wang, M. (2019). Transportation functionality vulnerability of urban rail transit networks based on movingblock: The case of Nanjing metro. Physica A: Statistical Mechanics and its Applications, 535, 122367.
    [CrossRef]   [Google Scholar]
  69. Zhang, N., Graham, D. J., Bansal, P., & Hörcher, D. (2022). Detecting metro service disruptions via large-scale vehicle location data. Transportation Research Part C: Emerging Technologies, 144, 103880.
    [CrossRef]   [Google Scholar]
  70. Zhang, S., Lo, H. K., Ng, K. F., & Chen, G. (2021). Metro system disruption management and substitute bus service: a systematic review and future directions. Transport Reviews, 41(2), 230-251.
    [CrossRef]   [Google Scholar]
  71. Zhang, S., Cheng, Y., Chen, K., Ma, C., Wei, J., & Hu, X. (2024). A general metro timetable rescheduling approach for the minimisation of the capacity loss after random line disruption. Transportmetrica A: Transport Science, 20(3), 2204965.
    [CrossRef]   [Google Scholar]
  72. Zhang, Y., Li, S., Yuan, Y., Zhang, J., & Yang, L. (2024). Approximate dynamic programming approach to efficient metro train timetabling and passenger flow control strategy with stop-skipping. Engineering Applications of Artificial Intelligence, 127, 107393.
    [CrossRef]   [Google Scholar]
  73. Zhang, Y., Yao, E., Wei, H., Zuo, T., & Liu, S. S. (2017). Constrained multinomial Probit route choice modeling for passengers in large-scaled metro networks in China. Transportation Research Procedia, 25, 2385-2395.
    [CrossRef]   [Google Scholar]
  74. Zheng, S., Liu, Y., Lin, Y., Wang, Q., Yang, H., & Chen, B. (2022). Bridging strategy for the disruption of metro considering the reliability of transportation system: Metro and conventional bus network. Reliability Engineering & System Safety, 225, 108585.
    [CrossRef]   [Google Scholar]
  75. Zhou, L., Yang, X., Wang, H., Wu, J., Chen, L., Yin, H., & Qu, Y. (2020). A robust train timetable optimization approach for reducing the number of waiting passengers in metro systems. Physica A: Statistical Mechanics and its Applications, 558, 124927.
    [CrossRef]   [Google Scholar]
  76. Zhuo, S., Miao, J., Meng, L., Yang, L., & Shang, P. (2024). Demand-driven integrated train timetabling and rolling stock scheduling on urban rail transit line. Transportmetrica A: Transport Science, 20(3), 2181024.
    [CrossRef]   [Google Scholar]

Cite This Article
APA Style
Gao, k., Wu, D., Zhang, S., Peng, R., & Wu, S. (2025). The State-of-the-Art Development and New Challenges: Operations Management of Metro Systems. ICCK Transactions on Systems Safety and Reliability, 1(1), 4–20. https://doi.org/10.62762/TSSR.2025.246708

Article Metrics
Citations:

Crossref

0

Scopus

0

Web of Science

0
Article Access Statistics:
Views: 34
PDF Downloads: 9

Publisher's Note
ICCK stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and Permissions
Institute of Central Computation and Knowledge (ICCK) or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
ICCK Transactions on Systems Safety and Reliability

ICCK Transactions on Systems Safety and Reliability

ISSN: pending (Online) | ISSN: pending (Print)

Email: [email protected]

Portico

Portico

All published articles are preserved here permanently:
https://www.portico.org/publishers/icck/